The paper solves the stochastic inverse optimal problem. Dynamic programming is used to transform the original problem into a differential equation. Such an equation is well-defined (with probability ...

It is proved that there exist stationary optimal plans for discounted dynamic programming problems, and that there exist semi-Markov ∈-optimal plans for positive dynamic programming problems. The ...

This course covers reinforcement learning aka dynamic programming, which is a modeling principle capturing dynamic environments and stochastic nature of events. The main goal is to learn dynamic ...

This paper proposes a new deep-learning-based algorithm for high-dimensional Bermudan option pricing. To the best of our knowledge, this is the first study of the arbitrary-order discretization scheme ...